There are some problems in the photovoltaic microgrid system due to the solar irradiancechange environment, such as power fluctuation, which leads to larger power imbalance and affects the stable operation of the microgrid. Aiming at the problems of power mismatch loss under partial shading in photovoltaic microgrid systems, this paper proposed a distributed maximum power point tracking (DMPPT) approach based on an improved sparrow search algorithm (ISSA). First, used the center of gravity reverse learning mechanism to initialize the population, so that the population has a better spatial solution distribution; Secondly, the learning coefficient was introduced in the location update part of the discoverer to improve the global search ability of the algorithm; Simultaneously used the mutation operator to improve the position update of the joiner and avoid the algorithm falling into the local extreme value. The results of the model in Matlab showed that the ISSA can track the maximum power point(MPP) more accurately and quickly than the perturbation observation method (P&O) and the particle swarm optimization (PSO) algorithm, and had good steady-state performance.INDEX TERMS Distributed maximum power point tracking, photovoltaic microgrid, sparrow search algorithm, spatial solution distribution, steady-state.
Aiming at the problem of high fluctuation and instability of photovoltaic power, a photovoltaic power prediction method combining two techniques has been proposed in this study. In this method, the fast correlation filtering algorithm has been used to extract the meteorological features having a strong correlation with photovoltaic power generation. The complete ensemble empirical mode decomposition with an adaptive noise model has been used to decompose the data into high and low-frequency components to reduce the data volatility. Then, the long short-term neural network and the deep confidence network were combined into a new prediction model to predict each component. Finally, the proposed combined photovoltaic power prediction method has been analyzed using an example and compared with the other prediction methods. The results show that the proposed combined prediction method has higher prediction accuracy.
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